triangulationalgorithms

This is a JPEG encoding & decoding program of still image. It does not use level shifting. Discrete Cosine transform (DCT) is performed both by classical & Chen s Flowgraph methods. Predefined JPEG quantization array & zigzag order are used here. RUN - LEVEL coding is used instead of Huffman coding. Compression ratio is compared for each DCT method. Effect of coarse and fine quantization is also examined. The execution time of 2 DCT methods is also checked. In addition, most energatic DCT coefficients are also applied to examine the effect in MatLab 7.4.0 R2007a. Input is 9 gray scale pictures & output is 9*9=81 pictures to compare. Blocking effect is obvious. (This is a JPEG encoding & decoding program of still image. It does not use level shifting. Discrete Cosine transform (DCT) is performed both by classical & Chen s Flowgraph methods. Predefined JPEG quantization array & zigzag order are used here. RUN - LEVEL coding is used instead of Huffman coding. Compression ratio is compared for each DCT method. Effect of coarse and fine quantization is also examined. The execution time of 2 DCT methods is also checked. In addition, most energatic DCT coefficients are also applied to examine the effect in MatLab 7.4.0 R2007a. Input is 9 gray scale pictures & output is 9*9=81 pictures to compare. Blocking effect is obvious. )

gray scale image denosiy matlab code

I THINK THAT ALL WHAT I WANT IS THIS

讲述了OFDM的一些基本知识，包含框图，及OFDM的优缺点等。对OFDM有个全面认识。(OFDM about some of the basics, including block diagram, and the advantages and disadvantages of OFDM and so on. Have a comprehensive understanding of OFDM.)

modulator for ofdm slmulation system

This program is designetd for find the invarient of the binary image

Network connectivity

sag model dvr and statcom

Image processing lab

计算机视觉大牛Andrew Blake著的书。 《视觉和图像处理中的马尔科夫随机场》 Markov random fields for vision and image processing(This book sets out to demonstrate the power of the Markov random field (MRF) in vision.It treats the MRF both as a tool for modeling image data and, coupled with a set of recently developed algorithms, as a means of making inferences about images. The inferences concern underlying image and scene structure to solve problems such as image reconstruction, image segmentation, 3D vision, and object labeling. This chapter is designed to present some of the main concepts used in MRFs, both as a taster and as a gateway to the more detailed chapters that follow, as well as a stand-alone introduction to MRFs.)